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Developing an equitable measure of parent engagement in early childhood education for urban schools

Author

Listed:
  • Gross, Deborah
  • Bettencourt, Amie F.
  • Holmes Finch, W.
  • Plesko, Corinne
  • Paulson, Rachael
  • Singleton, Demetria L.

Abstract

This study evaluated the validity of the Parent Engagement in Early Childhood Education (PEECE) Survey as an equitable measure of parent engagement in early childhood education that would address limitations of existing parent-report measures for use in urban schools in high poverty neighborhoods. We examined the PEECE Survey factor structure; item difficulty; discrimination; measurement invariance; and associations with children’s kindergarten readiness, school absenteeism, and teachers’ ratings of parent engagement among 304 parents of kindergarteners (74% mothers, 68.4% African American, 63.2% single parents, 59.2% low-income). Factor analyses revealed 3 PEECE Survey factors: Knowledge/Expectations, Trust/Communication, and Home-based Engagement (factor reliabilities ranged from 0.63 to 0.85). There was minimal evidence of differential item functioning by parent and child characteristics. PEECE subscale scores were significantly positively associated with kindergarten readiness and teacher ratings of parent’s engagement; and significantly negatively related to kindergarten absenteeism. Subscale scores did not differ by parents’ income level, marital status, or education or by child gender; African American parents reported higher scores on Knowledge/Expectations. Results suggest the 25-item PEECE Survey is a valid and equitable measure of parent engagement, capturing behaviors and perspectives that are feasible for parents with limited incomes and resources and linked to indicators of children’s early academic success.

Suggested Citation

  • Gross, Deborah & Bettencourt, Amie F. & Holmes Finch, W. & Plesko, Corinne & Paulson, Rachael & Singleton, Demetria L., 2022. "Developing an equitable measure of parent engagement in early childhood education for urban schools," Children and Youth Services Review, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:cysrev:v:141:y:2022:i:c:s0190740922002493
    DOI: 10.1016/j.childyouth.2022.106613
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    References listed on IDEAS

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    1. Klaas Sijtsma, 2009. "On the Use, the Misuse, and the Very Limited Usefulness of Cronbach’s Alpha," Psychometrika, Springer;The Psychometric Society, vol. 74(1), pages 107-120, March.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. Richard Hendra & Aaron Hill, 2019. "Rethinking Response Rates: New Evidence of Little Relationship Between Survey Response Rates and Nonresponse Bias," Evaluation Review, , vol. 43(5), pages 307-330, October.
    4. Yang, Mi-Youn & Harmeyer, Erin & Chen, Zibei & Lofaso, Blaine Masinter, 2018. "Predictors of early elementary school suspension by gender: A longitudinal multilevel analysis," Children and Youth Services Review, Elsevier, vol. 93(C), pages 331-338.
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